Automated and assisted identification of stroke using feature-based brain imaging

ABSTRACT

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

TECHNICAL FIELD

This document generally relates to identifying strokes from brain imagedata.

BACKGROUND

Stroke is a major cause of disability in the United States. Strokeoutcomes differ depending on where and how large the stroke event is.Acute ischemic strokes present with multiple restricted diffusionlesions scattered throughout a brain parenchyma, particularly embolicand watershed infarcts. The morphological variety and complexity of suchinfarcts can be high in terms of their potential number, shape, volume,and spatial distribution. Such infarcts can impose varying burdens andvariabilities on a subject who experiences the stroke event.

SUMMARY

This document generally describes using artificial intelligence (AI)based approaches, such as machine learning algorithms, techniques, andmodels, to diagnose stroke events, such as acute stroke. The disclosedtechnology provides a semi-automatic tool that employs AI-basedalgorithms to present users, such as doctors, researchers, and othermedical professionals, with validated and mapped brain stroke imagery.The disclosed technology can triage three-dimensional regions ofinterest in brain image data and provide quantitative measures about thebrain (e.g., number of strokes, size of stroke volumes, distribution ofstroke volumes in a brain, etc.) to help the users make informed,accurate, and quick decisions about stroke etiology, diagnosis, andtreatment. The AI-based techniques described herein can provide for moreaccurate detection of stroke events from image data, including but notlimited to Magnetic Resonance Imaging (MRI), computed tomography (CT).Performing the described semi-automatic AI-based techniques can expandon the applicability and effectiveness of different image data byfurther enhancing clinical decisions through more advanced imageanalysis, presentation, and interpretation. A clinician or otherrelevant user can use the disclosed technology to more accurately andeasily diagnose a subject's brain, stroke condition, and/or developappropriate treatment.

The disclosed techniques therefore provide an AI-guided approach usinghigh-performance computing, for fast (e.g., minutes or seconds) andprecise (e.g., sub-milliliter) detection and quantification of infarctedbrain volume(s), regardless of size and morphology, of the subject. Thisapproach can employ processing and analyzing original DICOM data (orother image data) via filtering, segmentation, and morphologicalneighborhood operations to identify and triage three-dimensional (3D)connected volumes of interest that can represent ischemic stroke orother stroke events. The disclosed technology can provide for immediatemeasurement of absolute (in ml) and relative (in terms of % of totalbrain volume) infarct burdens of complex strokes on initial imagingdata, which can otherwise be challenging to accomplish with existingtechnologies or manual human review of brain image data. Volumetricanalysis of stroke can represent a critical future biomarker. Thedisclosed technology can therefore be implemented into clinical andresearch settings to guide clinical diagnoses, therapeutic decisions,and outcome predictions using AI-based volumetric analysis.

In addition to the embodiments of the attached claims and theembodiments described throughout this disclosure, the following numberedembodiments are also innovative.

Embodiment 1 is a system for automated identification of volumes ofinterest in brain image data, the system including one or moreprocessors and computer memory storing instructions that, when executedby the processors, cause the processors to perform operations including:receiving image data of a brain having header data and voxel values,wherein the voxel values represent an interruption in blood supply ofthe brain when imaged, extracting the header data from the image data,populating an array of cells with the voxel values, applying asegmenting analysis to the array to generate a segmented array, applyinga morphological neighborhood analysis to the segmented array to generatea features relationship array, wherein the features relationship arrayincludes features of interest in the brain indicative of stroke,identifying three-dimensional (3D) connected volumes of interest in thefeatures relationship array, and generating output, for display at auser device, indicating the identified 3D volumes of interest.

Embodiment 2 is the system of embodiment 1, wherein the 3D volumes ofinterest are at least one of watershed infarcts and embolic infarcts inthe brain indicative of ischemic or hemorrhagic stroke.

Embodiment 3 is the system of any one of embodiments 1 through 2,wherein the image data of the brain is generated by at least one ofcomputed tomography (CT) and Magnetic Resonance Imaging (MRI).

Embodiment 4 is the system of any one of embodiments 1 through 3,wherein the image data of the brain includes time-series data of thebrain generated as a contrast dye.

Embodiment 5 is the system of any one of embodiments 1 through 4,further comprising applying a filtering analysis to the array togenerate a filtered array based on identifying a subset of the cells inthe array to exclude from the filtered array, and removing theidentified subset of the cells to generate the filtered array.

Embodiment 6 is the system of any one of embodiments 1 through 5,wherein applying the segmenting analysis comprises segmenting the cellsof the array based on physiological structures of the brain.

Embodiment 7 is the system of any one of embodiments 1 through 6,wherein applying the morphological neighborhood analysis comprisesidentifying features of interest in the segmented array based onconnectivity data amongst the cells in the segmented array.

Embodiment 8 is the system of any one of embodiments 1 through 7,wherein the operations further comprise storing, in a data store, theidentified 3D volumes of interest.

Embodiment 9 is the system of any one of embodiments 1 through 8,wherein identifying three dimensional (3D) connected volumes of interestcomprises counting voxel values within a predefined region of the imagedata to infer a volume of the brain, superimposing the identified 3Dvolumes of interest on the within the predefined region of the imagedata, and identifying relative infarct burden in percent of brain volumebased on determining a ratio between the superimposed 3D volumes ofinterest and the inferred volume of the brain.

Embodiment 10 is the system of any one of embodiments 1 through 9,wherein generating output comprises generating coronal and sagittalviews of the 3D volumes of interest in the brain image data.

Embodiment 11 is the system of any one of embodiments 1 through 10,wherein generating output comprises superimposing the 3D volumes ofinterest on the brain image data, and tinting the 3D volumes of interestin one or more indicia that is different than an indicia of the brainimage data.

Embodiment 12 is the system of any one of embodiments 1 through 11,wherein tinting the 3D volumes of interest comprises tinting a first ofthe 3D volumes of interest in a first indicia based on determining thata volume of the first of the 3D volumes of interest is greater than afirst threshold level, wherein the first indicia is a red color, tintinga second of the 3D volumes of interest in a second indicia based ondetermining that a volume of the second of the 3D volumes of interest isless than the first threshold level but greater than a second thresholdlevel, wherein the second indicia is an orange color, tinting a third ofthe 3D volumes of interest in a third indicia based on determining thata volume of the third of the 3D volumes of interest is less than thesecond threshold level but greater than a third threshold level, whereinthe third indicia is a yellow color, tinting a fourth of the 3D volumesof interest in a fourth indicia based on determining that a volume ofthe fourth of the 3D volumes of interest is less than the thirdthreshold level but greater than a fourth threshold level, wherein thefourth indicia is a green color, tinting a fifth of the 3D volumes ofinterest in a fifth indicia based on determining that a volume of thefifth of the 3D volumes of interest is less than the fourth thresholdlevel but greater than a fifth threshold level, wherein the fifthindicia is a blue color, and tinting a sixth of the 3D volumes ofinterest in a sixth indicia based on determining that a volume of thesixth of the 3D volumes of interest is less than a sixth thresholdlevel, wherein the sixth indicia is a purple color.

Embodiment 13 is the system of any one of embodiments 1 through 12,wherein identifying three-dimensional (3D) connected volumes of interestin the features relationship array is based on applying one or moremachine learning models that were trained using training datasets thatinclude training brain image data labeled with areas of interest andtraining brain image data labeled with areas that are not of interest.

Embodiment 14 is a method for performing any one of the embodiments 1through 13.

Moreover, provided herein are systems for automated identification ofvolumes of interest in volumetric brain images. The systems can includeone or more processors, and computer memory storing instructions that,when executed by the processors, cause the processors to performoperations including receiving a volumetric image of a brain thatincludes header data and voxel values representing an interruption inthe blood supply of the brain when imaged, extracting header data fromthe volumetric image, preprocessing the voxel values, populating anarray of cells with the preprocessed voxel values, applying a filteringanalysis to the array, applying a segmenting analysis to the array,applying a morphological neighborhood analysis to the array, andidentifying three dimensional (3D) connected volumes of interest in thearray.

The systems described herein can include one or more optional features.For example, the volumes of interest can represent ischemic orhemorrhagic stroke. In some implementations, the volumetric image can begenerated by computed tomography (CT). The volumetric image can also begenerated by Magnetic Resonance Imaging (MRI). As another example, thefiltering analysis can identify some, but not all, of the cells to beexcluded from future analysis. The segmenting analysis can includesegmenting the cells of the array based on physiological structures ofthe brain as recorded in the array. In some implementations, themorphological neighborhood analysis can include feature detection toidentify features of interest in the array based on connectivity ofcomponents represented in the array. In some implementations, the strokecan be an ischemic stroke. In some implementations, the stroke can be ahemorrhagic stroke.

As yet another example, the operations can further include displaying ona graphic user interface (GUI) the identified 3D connected volumes ofinterest. In some implementations, the operations further can includerecording in a datastore the identified 3D connected volumes ofinterest. Identifying three dimensional (3D) connected volumes ofinterest in the array can include a confidence interval. In someimplementations, populating an array of cells with the preprocessedvoxel values can include populating an array in MATLAB.

Provided herein are also methods for automated identification of volumesof interest in a volumetric brain image, the method including receivinga volumetric image of a brain that has header data and voxel valuesrepresenting an interruption in the blood supply of the brain whenimaged, extracting header data from the volumetric image, preprocessingthe voxel values, populating an array of cells with the preprocessedvoxel values, applying a filtering analysis to the array; applying asegmenting analysis to the array, applying a morphological neighborhoodanalysis to the array, and identifying three dimensional (3D) connectedvolumes of interest in the array.

The method can include one or more optional features. For example, thevolumes of interest can represent ischemic or hemorrhagic stroke. Thevolumetric image can be generated by computed tomography (CT). Thevolumetric image can be generated by Magnetic Resonance Imaging (MRI).The method can be performed on one or more processors, and computermemory storing instructions that, when executed by the processors, cancause the processors to perform the method. In some implementations, thevolumetric image can include time-series data generated as a contrastdye. In some implementations, the volumetric image can be a DICOM image.

Filtering analysis can include identifying some, but not all, of thecells to be excluded from future analysis. Segmenting analysis caninclude segmenting the cells of the array based on physiologicalstructures of the brain as recorded in the array. Morphologicalneighborhood analysis can include feature detection to identify featuresof interest in the array based on connectivity of components representedin the array.

In some implementations, the stroke can be an ischemic stroke. Thestroke can also be a hemorrhagic stroke.

The method can also include identifying multiple sets of threedimensional (3D) connected volumes of interest. The multiple sets ofthree dimensional (3D) connected volumes of interest can be taken over aperiod of time. The method can also include scoring volume-based strokeoutcome. In some implementations, the method can include generating anoutcome prediction. Identifying three dimensional (3D) connected volumesof interest in the array can include identifying measurement units. Themeasurement units can be milliliters. Identifying three dimensional (3D)connected volumes of interest in the array can also include identifyingsub-milliliter volumes of interest. In some implementations, identifyingthree dimensional (3D) connected volumes of interest in the array caninclude identifying relative infarct volume. In some implementations,identifying three dimensional (3D) connected volumes of interest in thearray can include identifying percentage of total brain volume.

The disclosed technology provides one or more of the followingadvantages. As an example, the disclosed technology provides foraccurate stroke detection through analysis of robust sets of informationand data. Information can be obtained and analyzed from examining apatient, collecting vitals such as heart rate, auscultate heart, andauscultate lungs, receiving information from other experts, analyzingvitals, lab reports, temperature, blood pressure, heart rate,respiratory rate, weight, glucose levels, INR, creatinine, Na, K, BUN,WBC, Hgb, Plt, reviewing and interpreting images from CT, CTA, CTP, andMRIs, and analyzing HPI, last known normal, onset of symptoms, pertinentpast medical history, TPA exclusion criteria, medications, AP/AC doses,allergies, implants, and medical devices. The amount of data to gather,review, and interpret is overwhelming. Accordingly, the disclosedtechnology provides for synthesizing the abundance of data mentionedabove to provide a user, such as a clinician, with more robust,automated, and accurate analysis into stroke diagnoses.

The disclosed technology also provides advanced image presentation andinterpretation for users, such as clinicians. For example, usingAI-based semi-automated imaging, a clinician can make improved decisionsregarding diagnosis and treatment of stroke in patients since theclinician can view the patient's brain from different angles/views thathighlight or visually represent volumes of interest indicative ofstroke.

As another example, the disclosed technology provides for fast andprecise detection and quantification of brain volumes, regardless ofsize and morphology. This can be advantageous to more accurately andquickly identify acute stroke in patients or other subjects. Moreaccurate and fast identification of stroke can also result in moreinformed decisions to be made, automatically by a computer system ormanually by a clinician, with regards to treatment.

As yet another example, the disclosed technology provides for preciseidentification of small sub-milliliter infarct volumes. These volumescan be critical in identifying stroke etiology. Identifying the tinyinfarct volumes can be accomplished by automatic, AI-based measurementof absolute (in ml) and relative (in terms of % of total brain volume)infarct burdens on initial brain image data. By automaticallyidentifying and quantifying such small infarct volumes, the disclosedtechnology can provide a clinician with more ability to accuratelydiagnose and treat stroke events.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a conceptual diagram of an example system for identifyingstroke in a patient using AI-based techniques.

FIG. 1B illustrates a schematic of an example system for performing thetechniques described herein.

FIG. 2 is a flowchart of a process for automatically identifying volumesof interest in brain image data that may be indicative of stroke.

FIG. 3 depicts example results of AI-based volumetry of watershed andembolic infarcts in a patient's brain.

FIG. 4 depicts example results of AI-based measurement of relativeinfarct burden in a patient's brain.

FIG. 5 depicts example AI-based coronal and sagittal views of detectedinfarcts in a patient's brain.

FIG. 6 is a system diagram of example computer components that can beused for performing the techniques described herein.

FIG. 7 shows a diagram of an exemplary computer processing system thatcan execute the techniques described herein.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This document generally relates to identifying strokes from brain imagedata using AI-based techniques and processes, such as machine learningalgorithms and models. The disclosed technology can be used to aidclinicians, medical professionals, and other users in 1) identificationof stroke mechanisms and/or etiology based on 3D-morphology of brainimage data, 2) measuring quantitative presentation and interval changesof ischemic and/or hemorrhagic strokes, 3) establishing a quantitativefoundation for a systematic approach to guide diagnostic and therapeuticapproaches, and 4) functioning as a biomarker for cerebrovasculardisease.

The disclosed techniques employ AI-enhanced imaging for diagnosis andtherapy of acute stroke in patients and other subjects (e.g., mammals,including humans and animals). The disclosed techniques provide foremploying semi-automated and/or automated high-performance computing formultidimensional (e.g., 3D volumes, multiple modalities, time, etc.)image processing. Therefore, the disclosed techniques can allow for moreaccurate and quicker detection and quantitative volumetric analysis ofischemic and hemorrhagic strokes in brain image data (e.g., CT headimage data, MRI brain imaging,). Using this analysis, users, such asclinicians, can make more accurate determinations about strokediagnosis, treatment, and therapeutics.

Referring to the figures, FIG. 1A is a conceptual diagram of an examplesystem 100 for identifying stroke in a patient using AI-basedtechniques. The system 100 includes a computer system 104, imagingdevice 106, user device 108, and data store 110, which can communicate(e.g., wired and/or wireless) via network(s) 102. In someimplementations, one or more of the computer system 104, imaging device106, user device 108, and data store 110 can be part of a same computer,system, and/or network of devices and/or computers. For example, theimaging device 106 and the user device 108 can be a same device.Moreover, the computer system 104 can be remote from the othercomponents of the system 100. The computer system 104 can also be partof a network of devices and/or systems, such as being part of a medicalfacility's infrastructure. The computer system 104 can also be acloud-based service.

Referring to FIG. 1A, image data of a subject's (e.g., patient) braincan be received at the computer system 104 (step A). The image data canbe received from the imaging device 106, the user device 108, and/or thedata store 110. In some implementations, the image data can betransmitted from the imaging device 106 once the image data is capturedby the imaging device 106. In some implementations, the image data canbe transmitted from the user device 108 when a clinician or otherrelevant user reviews the image data after it has been captured by theimaging device 106. The user can, for example, select an option at a GUIpresented at the user device 108 to process the image data. Selectingthis option can cause the image data to be transmitted to the computersystem 104 in step A. In some implementations, the image data can beretrieved from the data store 110 when the computer system 104 performsprocessing on the image data or a batch of image data at a time that islater than when the image data was initially captured. As describedthroughout this disclosure, the image data can be DICOM data. The imagedata can also be one or more other types of brain imaging data, such asCT scans, MRIs, and x-rays.

The computer system 104 can process the image data (step B). Processingcan include performing one or more analyses to refine the image data. Asa result of processing, the computer system 104 can more accurately makedeterminations about whether the image data indicates a stroke event forthe particular patient. As described further below, processing caninclude applying filtering, segmenting, and/or morphologicalneighborhood operations to the image data. Processing the image data canalso include extracting personally identifying information from theimage data (e.g., header data) to preserve patient privacy.

In some implementations, one or more machine learning models can betrained and used by the computer system 104 to process the image data.

Once the image data is processed, the computer system 104 can identifyvolumes of interest indicative of stroke in the image data (step C). Thecomputer system can employ techniques such as a flood fill algorithm toidentify volumes of interest. For example, the computer system can usethe flood fill algorithm to detect 3D connected volumes within a certainpixel threshold in the image data. The 3D connected volumes must meetcertain pixel upper and lower limits typically indicative of stroke inorder to be connected. In some implementations, such volumes may beconnected if they have 26 connectivity. The computer system may continueto determine whether 3D volumes of interest meet a same criteria ordefinition to be linked until the computer system runs into a barrier.The barrier can be a situation where two volumes of interest do notsatisfy a same definition, criteria, or rule. For example, the computersystem can determine whether each voxel has a density of a same valueand/or a density within some threshold range. As an illustrativeexample, in an array of cells, where each cell represents a voxel, anycells that are near each other and have densities of 1 (or anotherpredetermined value) can be identified as meeting the same criteria andthus connected. Any cells that are near each other and do not havedensities of 1 (or another predetermined value) may not be connected.

Accordingly, like pixels can be clustered together. The cluster of likepixels can then be measured to determine a total volume. The computersystem may also adjust sensitivity to identify different volumes ofinterest. Therefore, in some implementations, the computer system canidentify and connect volumes of interest that have a smallestpredetermined pixel size. The computer system can also identify andconnect volumes of interest having any other predetermined pixel size.Moreover, in some implementations, the computer system can utilize oneor more machine learning models to suggest settings, such as pixel size,criteria, definitions, or other rules that can be used to connect 3Dvolumes of interest.

The computer system can also be trained to scan the entire brain in theimage data to identify all volumes of interest, sort the volumes ofinterest by size (e.g., volume size), and then determine whether each ofthe sorted volumes (e.g., from largest volume to smallest volume) isindicative of a stroke.

Using the techniques described herein, the computer system 104 is ableto identify single voxel volumes, which means stroke determinations canbe made using a on a single voxel basis, which is the highest resolutionhighest resolution of an CT or MRI. This can be advantageous to detectall possible volumes of interest in the brain on a more granular level,which can otherwise be challenging or impossible.

In some implementations, the computer system 104 can utilize one or moremachine learning models to identify the volumes of interest inmultidimensional space. For example, the computer system 104 canidentify volumes of interest that are connected in 3D space. The modelscan, for example, be trained to identify portions of training image dataof brains that contain infarcts indicative of stroke events. The modelscan be trained to identify volumes of interest that are unique todifferent types of strokes. Such models can be continuously improvedbased on identifications made by the computer system 104 during runtime.Therefore, the models can be improved to more accurately detect strokeevents. The models can also be improved to accurately detect strokeevents by identifying even smaller volumes of interest. Refer to FIG. 7for further discussion about training the models.

The computer system 104 can determine a presence of stroke (step D).Once the volumes of interest are identified, the computer system 104 cananalyze such volumes and determine whether they in fact represent astroke event for the particular patient. The computer system 104 can,for example, assign confidence scores to each of the identified volumesof interest, where the confidence scores indicate likelihood that thevolume of interest is indicative of a stroke event. If any of theassigned confidence scores exceed a threshold level, the computer system104 can determine that a stroke event in fact exists for the particularpatient. The computer system 104 may also quantify a severity of thestroke event. The stroke event may be quantified based on the assignedconfidence score(s), a size of one or more volumes of interest, and/or apresence of multiple volumes of interest.

The computer system 104 can generate output (step E). The output caninclude visual representations of the patient's brain image data withthe identified volumes of interest overlaid and represented in anindicia different than the indicia of the patient's brain. Refer toFIGS. 3-6 for further discussion. The output can also include textual ornumeric information about the identified volumes of interest and thepresence of stroke. The output can include confidence scores that thepatient has a stroke event, predictions about effects that the strokeevent would have on the patient, predicted severity of the stroke eventfor the patient, and/or suggestions for diagnosis and/or treatment forthe patient. One or more other outputs can also be generated by thecomputer system 104.

The computer system 104 can transmit the output to the user device 108(step F). The user device 108 can display the output (step G). The userat the user device 108 can interact with the output, such as selectingdifferent views of the brain image data to view the identified volumesof interest relative to other portions of the brain. The user can alsouse the output to make informed and accurate decisions about thepatient's condition, diagnosis, and treatment. As a result, thecomputer-generated output can provide a semi-automatic solution to theuser that improves their efficiency and accuracy in analyzing apatient's brain, identifying stroke events, diagnosing the patient, andmaking other medical or clinical decisions.

The computer system 104 can store the generated output and/or strokedetermination in the data store 110 (step H). The output and/or strokedetermination can be retrieved and used in future analysis by thecomputer system 104 or another computing system (e.g., a cloud-basedcomputing system that is used by a hospital infrastructure). As anexample, the visual representations of the identified volumes ofinterest overlaying the image data can be used in future populationanalysis and research studies.

In some implementations, steps A-H can be performed on image data of onepatient's brain. Processing and analyzing the one patient's brain can beperformed in real-time, for example, at or around a same time that thepatient's brain is being imaged by the imaging device 106. Theprocessing and analyzing can also be performed some time after thepatient's brain is imaged. In some implementations, steps A-H can beperformed on a batch of image data representing multiple differentpatients' brains. Thus, image processing and analysis can be performedat once, which can more efficiently utilize available computingresources.

FIG. 1B illustrates a schematic of the example system 100 for performingthe techniques described herein. The system 100 can be used forautomated identification of volumes of interest in volumetric brainimages or other brain image data. A patient 120 can be imaged by theimaging device 106 in order to gather data that can be used to identify,by the computer system 104, stroke infarct volume in the patient 120'sbrain. For example, the patient 120 can be subjected to an x-ray, a CTscan, an MRI scan, or any other type of imaging performed by the imagingdevice 106. The patient 120 can be a mammal, such as a human or ananimal.

Imaging the patient 120 can produce one or more image data 122. In someimplementations, imaging the patient 120 can result in generation ofDICOM images. Other types of images may also be generated, such as brainimage data that is derived from CT scans, MRIs, and x-rays. The DICOMimages, for example, capture many two dimensional (2D) slices of thepatient 120's brain that can be stacked to create 3D volumetric images.In some cases, the 2D slices can also be used to generate 4D volumetricimages in a time domain. Such volumetric images can be utilized by thecomputer system 104 to more accurately determine different volumes ofinterest (e.g., infarcts) in the patient 120's brain.

In addition, the DICOM images (or other brain image data 122) mayinclude header data 124, such as a manufacturer of the imaging device106, a timestamp of the imaging, etc. The header data 124 may or may notprovide information about the patient 120's condition or brain. In someimplementations, the header data 124 may include private informationabout the patient 120 or other personally identifying information, suchas the patient 120's age, weight, name, birthdate, and/or uniqueidentifier.

As described herein, the computer system 104 can receive the image data122 (e.g., the DICOM images) and extract the header data 124. Extractioncan be performed using known techniques, software, and/or applications.One or more machine learning algorithms and/or models can also be usedby the computer system 104 to automatically identify header data 124 (orparticular portions of the header data 124 that should be removed, suchas personally identifying information) and extract the header data 124.In some cases, portions of the header data 124 may be discarded (e.g.,redundant data, private information not needed for the disclosedtechniques and processes, other personally identifying information,etc.). Supplementary information may be generated in part by using theextracted header data 124. The supplementary information can includedata about the patient 120 that can populate health records associatedwith the patient 120. Supplementary information can also include dataabout the imaging that was performed, such as a time and data, which canbe useful if a clinician seeks to compare image data analysis from onetime period to another time period. The header data 124 may also beaggregated, compressed, and/or used to look up other data about thepatient 120, the imaging device 106, and/or the patient 120's condition.In some implementations, the header data 124 can be used to glean moreinformation about the patient 120 and to more accurately diagnose andtreat the patient 120's condition.

The computer system 104 may also access additional image information inthe image data 122. As an example, the computer system 104 can accessthe image data of the DICOM images. This image data may include pixel orvoxel values that store information reflecting phenomenon of the patient120 at the time of imaging. For example, in some cases, imagingperformed by the imaging device 106 may generate one or more pixelvalues that correspond to fat tissue, one or more other pixel valuesthat corresponds to contrast dye, one or more other pixel values thatcorresponds to bone, etc. This information can be used by the computersystem 104 to more accurately identify volumes of interest in thepatient 120's brain (e.g., by analyzing and correlating pixel values orvoxel values), to determine a condition of the patient 120's brain, andto provide for more accurate diagnosis and treatment determinations.

The computer system 104 may generate, from the header data 124 and theimage data 122, one or more useful outputs. The outputs can be used by aclinician to more accurately and quickly analyze the patient 120's brainand make diagnosis and treatment decisions. One such output can be agraphical representation 126 of the patient 120's brain with possiblestroke areas (e.g., volumes of interest) highlighted or identified insome other indicia. For example, the computer system 104 may reserve oneor more colors (e.g., bright orange or red) for portions of the brainthat the computer system 104 identifies, using machine learning models,as likely indicative of stroke events or volumes of interest. Thecomputer system 104 can be configured to tint one or more pixels of theidentified portions of the brain in the graphical representation 126such that when the graphical representation 126 is viewed by theclinician, the clinician's attention is drawn/directed to the tintedportions. The clinician, therefore, is directed to review the particulartinted portions in the graphical representation 126. Such visualindicators can be advantageous to reduce an amount of time needed by theclinician to analyze the patient 120's entire brain and locate volumesof interest therein. Moreover, the visual indicators make it easier toidentify fine details or shapes indicative of possible stroke eventsthat may be difficult to quickly spot or identify at all with the humaneye. Rather, the clinician can direct their attention and expertise tothe tinted portions in the graphical representation 126 of the brain,and spend their time appropriately diagnosing and treating the patient120's condition.

Another example output is a stroke analysis 128 that can include textualand/or numerical information about the patient 120's brain. The strokeanalysis 128 and the graphical representation 126 of the brain can bothbe outputted at a user device of the clinician (e.g., the user device108 in FIG. 1A). In some implementations, the stroke analysis 128 or thegraphical representation 126 can be outputted. In some implementations,the clinician can select a preferred form of output, which can then beoutputted and presented at the user device of the clinician.

The textual and/or numerical information of the stroke analysis 128 canbe determined by the computer system 104 using AI techniques, asdescribed herein. For example, a stroke-risk score may be calculated forthe patient 120, using one or more machine learning models. Thestroke-risk score can represent a computed likelihood that volumes ofinterest identified in the image data 122 are indicative of stroke forthe patient 120. The stroke-risk score may be a value on a scale of 0to 1. One or more other scales or ranges of values can also be used(e.g., 0 to 100, Boolean or string values such as “true” and “false,”etc.).

As described herein, the computer system 104 can be trained, using AItechniques such as machine learning trained models, to identify portionsin the image data 122 that may be indicative of stroke (e.g., infarcts,volumes of interest) and assign a confidence value as to how likely theidentified portions are associated with stroke (e.g., the stroke-riskscore). The confidence value can be compared, by the computer system104, to a threshold value. If the confidence value is greater than thethreshold value, the computer system 104 can classify the patient 120'sbrain as likely having indications of stroke. In such a case, thegraphical representation 126 can also be augmented to provide a visualindication to the clinician about the possible stroke. The visualindication can include but is not limited to a warning graphic, anaudible alarm, etc. One or more other outputs can also be generated bythe computer system 104 using the techniques described throughout thisdisclosure.

FIG. 2 is a flowchart of a process 200 for automatically identifyingvolumes of interest in brain image data that may be indicative ofstroke. The process 200 can be performed by the computer system 104. Theprocess 200 can also be performed by one or more other computers,systems, servers, network of servers, devices, and/or cloud-basedservices. For illustrative purposes, the process 200 is described from aperspective of a computer system.

Referring to the process 200, image data of a brain is received in 202.A DICOM image and/or multiple DICOM images can be received from animaging device (e.g., the imaging device 106 depicted in FIG. 1A), asdescribed above. One or more other types of images can be received. Thecomputer system 104 can be physically remote and communicably coupled tothe imaging device 106 and may receive, over a secure computer network,a DICOM image created by the imaging device 106. The image can begenerated using one or more technologies such as CT, MRI, and/or othertechnologies.

The image can include a variety of additional information. For example,the image can include time series data. A patient of the imaging can beadministered a contrast dye. Imaging may be recorded as the dye washesinto the subject's brain such that the dye is saturated in the brain.Imaging may also be recorded as the dye washes out of the brain. Thecontrast dye can make portions of the brain more apparent anddifferentiated from other portions of the brain in the image. The imagecan include other information, such as header data. The header data canindicate information about the imaging device, when the image wascaptured, what settings were used to capture the image, as well asinformation about the patient whose brain is imaged. The header data,for example, can include personally identifying information, such as thepatient's name, age, date of birth, and/or patient identifier.

Accordingly, header data is extracted in 204. Data in the headerrelevant to processes described throughout this disclosure can becopied, stored in another data file or object, and/or used in otherprocesses and analyses. Other data in the header, such as personallyidentifying information, may be ignored, discarded, or otherwise removedfrom the image data. For example, header data may be extracted to complywith personal health information requirements, such as HIPPA or othersecurity requirements, such as FERPA or FISMA. Sometimes, header datamay be extracted if it obstructs imaging views of the brain, therebymaking subsequent image processing more accurate.

The image data can be preprocessed in 206. For example, image data ofthe DICOM image or DICOM images can be preprocessed. The image data caninclude voxel or pixel values in addition to the header data. Forexample, the volumes can be determined by an MRI scanner and can beincluded in DICOM data (e.g., header data). Each of the voxel values canrepresent different objects or portions in the patient's brain. When thevoxel values are counted, grouped, and analyzed by the computer system(e.g., using machine learning models and other AI techniques describedthroughout this disclosure), the voxel values can reveal informationabout a condition of the patient's brain. For example, some voxel valuescan be grouped by the computer system into volumes of interest thatrepresent infarcts. Grouping the voxel values can be beneficial tocalculate stroke volumes and estimate actual brain volume. Thecalculated stroke volumes and actual brain volume can be used by thecomputer system to measure a stroke in ml measurement and/or percent ofbrain volume. The computer system can then analyze these grouped valuesto determine whether the patient has a stroke event, risk of having astroke, and/or a relative burden of the infarcts on the brain. Asanother example, some voxel values can be grouped into volumes ofinterest that represent an interruption in blood supply of the brain ata time of imaging. The computer system can glean further insight intothe condition of the patient's brain based on identifying thisinterruption in blood supply.

An array can be populated with voxel values from the preprocessed imagedata in 208. For example, for a 4D image, a 4D array of a same size maybe created and values determined for the voxels may be placed into thecorresponding cells of the 4D array. The array can be generated in oneor more other dimensions, including but not limited to 2D, 3D, 5D, etc.

The computer system can perform a filtering analysis on the array togenerate a filtered array (210). Filtering can include identifying asubset of cells in the array to exclude from the array and removing theidentified subset of the cells to generate the filtered array. Forexample, some but not all of the cells of the array may be tagged asfiltered out of future analysis. In some implementations, the computersystem can be trained, using AI or machine learning techniques, toidentify features of interest and/or identify features that are not ofinterest in the array. For example, machine learning models can betrained using training data sets to identify features of interest.Anything that has not been identified as features of interest can bediscarded from the array. Machine learning models can also be trained toidentify features that are not of interest, to select such features inthe array, and to update the array by removing such features from thearray. Machine learning and other AI-based techniques can also be usedto improve the computer system's accuracy in identifying features ofinterest and/or features that are not of interest and removing thefeatures that are not of interest from the array. The features not ofinterest may include cells representing locations outside of thesubject's head (e.g., the subject's neck or shoulders). Since featuresthat are not of interest are removed from the array, the computer systemcan more efficiently utilize computing resources by only analyzing cellsin the array that correspond to features of interest. In someimplementations, filtering can be optionally performed.

The computer system can also perform a segmenting analysis to generate asegmented array (212). The segmenting analysis can be performed on thefiltered array from block 210. Sometimes, the filtering analysis may notbe performed, and the computer system may only perform the segmentinganalysis. In some implementations, the computer system may perform thesegmenting analysis before the filtering analysis. Applying thesegmenting analysis can include segmenting the cells of the filteredarray based on physiological structures of the brain. Like the filteringanalysis, machine learning or other AI-based techniques and/or modelscan be used by the computer system in some implementations to accuratelyidentify and group cells in the array based on known structures in thebrain. One or more machine learning models can, for example, be trained,using training data sets, to identify different types of physiologicalstructures of the brain based on annotated and labelled voxel values.Output from the models can be groupings of cells in the array, whereeach grouping corresponds to a different physiological structure of thebrain.

Using the techniques described herein, the computer system can, forexample, segment the cells of the filtered array into various volumetricgeometries that represent structures of the patient's brain andsurrounding tissue in the brain. This can include blood vessels, braintissue, bone, etc. This segmenting analysis may be limited only to thecells that have not been filtered out in the previous filtering process.As mentioned above, in some implementations, the segmenting analysis canbe performed on all cells in the array, and the analysis can be targetedat identifying particular physiological structures of interest in thebrain. Moreover, performing segmenting analysis can be beneficial toensure that the computer system accurately analyzes areas of interest inthe brain for indications of stroke.

The computer system can perform morphological neighborhood analysis onthe array to identify features of interest in the brain (214). As aresult of this analysis, the computer system can generate a featuresrelationship array. The features relationship array can include featuresof interest in the brain that are indicative of stroke. Performingmorphological neighborhood analysis can include identifying features ofinterest in the segmented array based on connectivity data amongst thecells in the segmented array. In some implementations, the morphologicalneighborhood analysis can be performed on the filtered array from block210. In yet some implementations, the morphological neighborhoodanalysis can be performed on the array from block 208.

As described above with regards to the filtering and segmentinganalyses, in some implementations, the computer system can utilize oneor more machine learning models that are trained, using brain imagetraining data sets, to identify connectivity data and make associationsbetween connectivity data of the cells in the array (e.g., remainingcells in the segmented array). The models can be trained to identifyconnectivity data that exists within some predetermined threshold range.If the connectivity data exists within the predetermined thresholdrange, the computer system can determine that the connectivity data isassociated with a particular feature of interest. That feature ofinterest can be an infarct that is commonly associated with strokeevents. As a result of morphological neighborhood analysis, the computersystem can group cells of the array that indicate features of interest.Grouping the cells can be advantageous to generate/visualize 3D volumesof the features of interest, which can further be used by the computersystem and/or a clinician to analyze and diagnose the patient'scondition.

Accordingly, the computer system can identify 3D connected volumes inthe brain that are indicative of stroke in 216. In some implementations,the computer system can identify connected areas of interest in thebrain in any other dimension, including but not limited to 2D, 4D, 5D,etc. Using techniques such as a flood fill algorithm, machine learningmodels, and/or other AI-based techniques, the computer system candetermine information about the 3D connected volumes. For example, insome implementations, one or more of the models can be trained withtemplate shapes that represent possible stroke volumes in a brain. Themodels can be trained to match identified volumes of interest in thebrain image data to the template shapes. If there is a match, thecomputer system can determine that the matching volume of interest isindicative of a stroke. The template shapes can also correspond todifferent types of strokes or different symptoms, diagnoses, treatments,and/or side effects. Thus, when a volume of interest is identified to bematching a template shape, the computer system can generate output thatincludes information associated with the matching template shape (e.g.,type of stroke, potential symptoms, potential diagnosis, potentialtreatments, potential side effects, etc.).

As another example, in 216, the computer system can determine a volumeof each identified feature of interest, a relative burden of the volumeon a total volume of the brain, what type of infarct or otherphysiological structure is represented by the 3D connected volume(s)(e.g., watershed infarcts, embolic infarcts, etc.), etc. The computersystem can also classify and identify what type of stroke the patientmay experience, such as ischemic or hemorrhagic stroke.

In some implementations, identifying the 3D connected volumes ofinterest can include determining a volume of the feature of interestrepresented by the 3D connected volumes of interest. For example, thecomputer system can determine in infarct volume. Determining the volumecan be based on counting voxel values within a predefined region of theimage data, where that predefined region corresponds to the feature ofinterest (e.g., an infarct). The computer system can also determine atotal volume of the brain based on counting voxel values within a regionof the image data that corresponds to the patient's brain. Thus, voxelvalues in space surrounding the brain may not be counted. Moreover, thecomputer system can determine relative infarct burden in percent ofbrain volume for each of the 3D volumes of interest. The relativeinfarct burden can be a ratio between each of the 3D volumes of interestand the computed total volume of the brain.

As yet another example, in 216, the computer system can determine a typeof stroke from the 3D connected volumes of interest in the brain. Thestroke can be an ischemic stroke, a hemorrhagic stroke, or a transientischemic attack. One or more machine learning models can be trained toclassify identified volumes of interest with identified types ofstrokes. Output of the models can be an indication of the type of strokerepresented by the 3D connected volumes of interest in the brain imagedata.

Moreover, identifying the 3D connected volumes of interest can includedetermining a confidence interval or value that indicates likelihoodthat a particular volume of interest (e.g., shape in the array)represents a stroke event or infarct. The confidence interval can alsoindicate likelihood that a collection of volumes of interest representsa stroke event or infarct. The higher the confidence interval (e.g., theconfidence interval exceeds some predetermined threshold value), themore likely identification of the stroke event or infarct is accurate.The lower the confidence interval (e.g., the confidence interval is lessthan some predetermined threshold value), the less accurate the one ormore models are in identifying stroke events or infarcts (e.g., thecomputer system is less certain that it made an accurate identificationusing the models). The confidence interval can then be used, by thecomputer system or another computing system, to continuously train andimprove accuracy of the models.

The 3D volumes of interest can be measured in unit measurements. Forexample, the measurement units can be milliliters or cubic centimeters.As a result, the computer system can make more granular identificationsof volumes of interest, thereby resulting in more accuratedeterminations of stroke events in any brain. The computer system candetect smaller features in the brain indicative of stroke that the humaneye may otherwise not be able to readily identify or analyze.

In 218, the computer system can generate output. As part of block 218,results of the process 200 may be recorded in computer memory, displayedin a GUI presented at a user device (e.g., the user device 108),transmitted to another system for further processing, analysis, and/orstorage, and/or otherwise used by a computer or other device. Theresults of the process 200 can be stored in a data store (e.g., the datastore 110). The results can then be retrieved from the data store atanother time for further analysis and processing. Moreover, the computersystem can generate output for display at the user device (e.g.,clinician's mobile device, smartphone, laptop, tablet, computer, etc.)that indicates the identified 3D volumes of interest. The output caninclude visual representations of the features in the brain that areindicative of a stroke event and/or infarct volumes. The visualrepresentations can include the brain image data overlaid with theidentified 3D volumes of interest, where the identified 3D volumes ofinterest can be tinted, colored, or highlighted in a color that isdifferent than the original colors of the brain image data. Refer toFIGS. 3-6 for further discussion on visual output. One or more otherindicia can also be used.

For example, the computer system can superimpose the identified 3Dvolumes of interest on predefined regions of the image data. In otherwords, features of interest that were identified in block 214 can bemapped onto (e.g., overlaid on) the image data that was received in 202.The image data can therefore be updated to visually depict the featuresof interest, which can be indicators of stroke (e.g., infarcts). Asdescribed throughout this disclosure, the features of interest can betinted one or more different colors to bring a user's attention to thoseareas in the image data. For example, features of interest havingvolumes that are within a first range (e.g., highest volume) can betinted a first color, such as red. Features of interest having volumesthat are within a second range (e.g., medium-sized volume) can be tinteda second color, such as green. Features of interest having volumes thatare within a third range (e.g., smallest volume) can be tinted a thirdcolor, such as purple. The different colors can be beneficial to directthe user's attention and analysis to the features of interest having thehighest volumes, which may have a larger impact on the condition of thepatient's brain, in comparison to features of interest having thesmallest volumes in the patient's brain. Regardless, color-coding thefeatures of interest can also be beneficial to direct the user'sattention to the features of interest so that the user does not have tospend their time analyzing all portions of the patient's brain in theimage data

If no features of interest are identified in block 214, for example,then no features of interest may be superimposed on the brain image datain block 218. In other words, white and grey matter in the brain imagedata may not be altered or modified. No features in the brain may betinted or otherwise shaded a color that is reserved for the varyingvolumes of interest described above

As another example, the computer system can generate as output, fordisplay at the user device, coronal and sagittal views of the 3D volumesof interest in the brain image data. The coronal and sagittal views canbe used, automatically by the computer system or manually by theclinician or other user, to determine stroke etiology. The coronal andsagittal views can also be used by the clinician or other relevantstakeholder to make determinations about the patient's diagnosis,treatment, and/or stroke etiology. Such additional views can make iteasier for the clinician to view and analyze the patient's brain fromdifferent perspectives. The clinician can therefore have a morewholesome view and understanding of the patient's brain, which benefitsthe clinician in more accurately diagnosing and treating the patient'scondition.

In some implementations, the process 200 can be performed multiple timesfor the same patient in order to identify how a stroke event or infarctschange over time for that particular patient. For example, volumetricimages of the patient's brain can be taken at a series of time points(e.g., time course) in which the stroke event or infarct volume can bemeasured within each time point to identify how the stroke event orinfarct volume changes over one or more different periods of time.

In some implementations, the process 200 can also include volume-basedstroke scoring. The larger the stroke event or infarct, the larger thevolume-based stroke score. The score can be an extent of damage to thepatient's brain and overall health or medical condition. The score canbe measured in terms of ml measurements or percent of total brainvolume. Thus, a volume of interest (e.g., stroke) that has a score valueof 2% of total brain volume can indicate that 2% of the patient's totalbrain volume is lost from the identified stroke event. In someimplementations, the score may be likelihood of stroke for theparticular patient.

In some implementations, the process 200 can also include generating anoutcome prediction. The outcome prediction may include predictions ofthe patient's physical or cognitive abilities after the stroke eventthat has been identified using the process 200. The computer system canuse one or more machine learning models that have been trained usinglabelled/annotated training data to correlate severity of stroke eventswith consequences on the patient's health. Using such models, thecomputer system can more accurately predict side effects that thepatient may experience based on their stroke event(s).

In yet some implementations, the computer system can make predictionsregarding effectiveness of various therapies for the particular strokeevent that the patient experienced. The computer system can use one ormore machine learning models, AI techniques, and patient data about thepatient to predict their response to different therapies. Sometimes, thecomputer system can also make suggestions about what therapies should beprescribed to the patient based on the predicted responses to differenttypes of therapies.

FIG. 3 depicts example results of AI-based volumetry of watershed andembolic infarcts in a patient's brain. Identified infarcts can beordered by volume and pseudo-colored in one or more color sequences.Here, a 6-color sequence is used: red-orange-yellow-green-blue-purple.Red represents a largest identified volume of the infarct(s) and/or avolume that is greater than a first predetermined threshold level.Purple represents a smallest identified volume of the infarct(s) and/ora volume that is less than another predetermined threshold level. Insome implementations, if more than 6 volumes are identified, the colorsequence can start over with red, and can repeat until all volumes havebeen identified and pseudo-colored. One or more other color sequencesand/or indicia (e.g., patterns, straight lines, dotted lines, othertextures, etc.) can be used to visually depict the different infarctvolumes in the brain.

Image data 300 depicts AI-based volumetry of watershed infarcts usingthe techniques described throughout this disclosure (e.g., refer toFIGS. 1-2 ). 5 ischemic sub-volumes have been identified by the computersystem 104. For example, the computer system 104 can apply a machinelearning model to the brain image data that is trained to identifywatershed infarcts. The computer system 104 has also identified anoverall infarct volume of 23.6 ml and a relative infarct volume of 2% ofthe total brain volume of 1,152 ml. These values indicate that becauseof the identified stroke, the patient has lost 2% of their totalmetabolically-active brain volume. The computer system 104 can apply oneor more machine learning models to the brain image data to computevolumes of the identified watershed infarcts and the total volume of thebrain. Moreover, the computer system 104 can apply one or more machinelearning models to tint/colorize portions of the brain image data thatrepresent the identified watershed infarcts. The portions of the brainimage data can be tinted based on the identified volume of each of thewatershed infarcts. Thus, red portions of the brain image data canrepresent the largest volume watershed infarcts that were identified bythe computer system 104 and one or more other colors can be used torepresent different volume sizes of the identified infarcts.

Image data 302 depicts AI-based volumetry of embolic infarcts using thetechniques described throughout this disclosure. Here 20 ischemicsub-volumes have been identified by the computer system 104 with anoverall infarct volume of 51.6 ml and a relative infarct volume of 4.4%of the total brain volume of 1,178 ml. Here, the patient has lost 4.4%of their total brain volume due to the identified stroke. Suchdeterminations are made by the computer system 104 when one or moremachine learning trained models are applied to the raw brain image data,as described throughout this disclosure.

The image data 300 demonstrates fewer volumes of watershed infarctsoverlaid on the brain image data. The identified volumes of interest arepseudo-colored red and orange, with very small/discrete portions orvolumes of interest pseudo-colored green. The image data 302demonstrates significantly more volumes of embolic infarcts overlaid onthe brain image data. The identified volumes of interest arepseudo-colored using the 6-color sequence. Thus, the overall infarctvolume of 51.6 ml is comprised of varying volumes of infarcts throughoutthe brain, as depicted in red, orange, yellow, green, blue, and purpleindicia.

FIG. 4 depicts example results of AI-based measurement of relativeinfarct burden in a patient's brain. Image data 400 depicts the AI-basedmeasurement of relative infarct burden. Diffusion-weighted imaging (DWI)is used in the example of FIG. 4 . DWI sequences represent both grey 402and white 404 matter in a patient's brain. DWI shows little signal incerebrospinal fluid (CSF), bone, muscle, fat, and connective tissuespaces. Thus, the DWI can be used for simple DWI-based brain volumetrydeterminations using the techniques described throughout. As discussedthroughout this disclosure one or more other types of image data canalso be used for brain volumetry determinations.

The computer system 104 can perform steps such as counting all voxelswithin predetermined DWI range or ranges in order to infer and calculatebrain volume. Brain volume 406 can be represented in a first indicia,such as a green color. Here, the computer system 104 determined that thebrain volume is 1,124 ml. Volumes of interest, or infarcted regions,that had been identified by the computer system 104 using the techniquesdescribed throughout this disclosure can be superimposed or overlaid onthe brain volume 406. Infarcted regions 408 can be visually representedin a second indicia, such as a red color. The infarcted regions 408 canalso be depicted in different indicia (e.g., different colors) dependingon their identified volumes (not depicted in FIG. 4 ). The computersystem 104 can infer the infarct volumes based on counting voxels in theinfarcted regions 408. Here, the computer system 104 determined that thetotal infarct volume is 121 ml. The computer system 104 can thendetermine a ratio of voxels in a first indicia (e.g., a red color) tovoxels in a second indicia (e.g., a green color) in order to determine arelative infarct burden in terms of percent of total brain volume. Thevoxels in the first indicia can represent identified infarcts. Thevoxels in the second indicia can represent a total brain volume (e.g.,grey matter). Accordingly, in the example of FIG. 4 , the computersystem 104 determines that the relative infarct burden is 10.8% (theratio of red-colored or tinted voxels, representative of infarcts, togreen-colored or tinted voxels, representative of brain volume). Thisinformation can be visually presented to a user, such as a clinician orother medical professional, and used to make more accurate decisionsabout the patient's condition, including their diagnosis and treatment.

FIG. 5 depicts example AI-based coronal and sagittal views of detectedinfarcts in a patient's brain. Image data 500 depicts the AI-basedcoronal and sagittal views of the detected infarcts. As described above,different infarct volumes are depicted in different indicia or colorsbased on their volumes. The color sequence used in FIG. 5 isred-orange-yellow-green-blue-purple. Red indicates a largest identifiedinfarct volume while purple indicates a smallest identified infarctvolume. Each of the remaining colors (orange, yellow, green, and blue)can be used to indicate varying ranges of identified infarct volume. Oneor more other color sequences or indicia (e.g., patterns, othertextures) can be used to visually represent the different infarctvolumes in a patient's brain.

Here, the DWI stacks from FIG. 3 are permuted, by the computer system104, into virtual sagittal and coronal slices (e.g., views). Such slicescan provide a user with additional views for more accurately analyzingdifferent portions of the patient's brain. Simultaneous review of theidentified volumes of interest in axial, sagittal, and coronalorientations can provide some advantages to the user. For example, thedifferent views can result in higher confidence in accepting orverifying certain volumes of interest as ischemic infarcts or rejectingthem as non-infarct volumes. This can lead to more accurate diagnosesand treatments. As another example, the different views provide forimproved and more accurate inferences of a suspected stroke etiology forthe particular patient (e.g., embolic, watershed, lacunar). Therefore,more accurate determinations can be made with regards to diagnosis andtreating a patient as well as predicting health-related repercussionsthat the patient may experience due to their identified stroke event.

FIG. 6 is a system diagram of example computer components that can beused for performing the techniques described herein. The computer system104, imaging device(s) 106, user device 108, and data store 110 cancommunicate via the network(s) 102. Moreover, one or more of thecomputer system 104, imaging device(s) 106, user device 108, and datastore 110 can be integrated or otherwise part of a same computer system,network, and/or cloud-based service.

The imaging device(s) 106 can be configured to capture images of asubject's brain. The imaging device(s) 106 can be any type of device forimaging the brain, as described throughout this disclosure. Exampleimaging device(s) 106 include but are not limited to CT scans, MRIs, andx-rays. Other imaging devices are also possible. Images captured by theimaging device(s) 106 can be transmitted to the computer system 104 forprocessing and analysis, the user device 108 for viewing and analysis,and/or the data store 110 for storage and future retrieval.

The data store 110 can store information that is used by the computersystem 104 to process and analyze the brain image data. In someimplementations, the information can stored across multiple data stores,databases, repositories, and/or cloud-based storage. The data store 110can store information such as brain image data 618A-N, patientinformation 620A-N, stroke identification models 622A-N, and trainingdata sets 624A-N.

The brain image data 618A-N can include images that are captured by theimaging device(s) 106. In some implementations, the brain image data618A-N can first be processed to remove personally identifyinginformation, thereby preserving patient privacy and complying withhealth privacy policies. The brain image data 618A-N can then be storedin the data store 110.

The patient information 620A-N can include health records about patientswhose brains are imaged. The patient information 620A-N can be linked tothe brain image data 618A-N. The patient information 620A-N can alsoinclude determinations made by the computer system 104. For example, ifthe computer system 104 identifies a stroke event in the brain imagedata 618A-N, the computer system 104 can generate an indication that astroke event is detected, and then can store that indication in thecorresponding patient information 620A-N. In some implementations, thepatient information 620A-N can also include information that is inputtedby a clinician or other relevant stakeholder at the user device 108. Forexample, if the clinician makes a diagnosis or prescribes some treatment(e.g., based on reviewing the brain image data 618A-N and/oroutput/determinations made by the computer system 104), the diagnosis orprescription can be inputted by the clinician at the user device 108 andstored in the corresponding patient information 620A-N in the data store110. The patient information 620A-N can be accessed by the user device108, the computer system 104, and/or other systems and/or devices thatare in secure communication with the data store 110 or part of a samehealthcare infrastructure.

The stroke identification models 622A-N can be machine learning modelsused by the computer system 104 to analyze the brain image data 618A-N.Thus, the computer system 104 can use one or more of the models 622A-Nto determine whether stroke events are present in the brain image data618A-N. For example, the models 622A-N can be trained to identifyvolumes of interest in the brain image data 618A-N, where such volumesof interest are indicative of stroke events. One or more of the models622A-N can also be trained to determine brain volume, infarct volume,and relative infarct burden on the brain volume using the brain imagedata 618A-N. The models 622A-N can be trained to perform differentfunctions/operations. For example, one or more of the models 622A-N canbe trained and used to process the brain image data 618A-N. One or moreof the models 622A-N can be trained and used to identify volumes ofinterest in the brain image data 618A-N. One or more of the models622A-N can be trained and used to make determinations about whether theidentified volumes of interest are indicative of stroke events. One ormore of the models 622A-N can also be trained to generate coronal andsagittal views of detected volumes of interest in the brain image data618A-N.

In some implementations, one or more of the models 622A-N can be trainedto identify different types of infarcts and/or volumes of interest thatare related to different types of strokes. For example, one or more ofthe models 622A-N can be trained and used to identify watershed infarctswhile one or more other models are trained and used to identify embolicinfarcts. The models 622A-N can be trained by the computer system 104(or another computing system) using the training data sets 624A-N.

The training data sets 624A-N can comprise large data sets of trainingsamples. The training samples can be brain image data, such as the brainimage data 618A-N. Some of the training samples can be brain image datawhere stroke events were detected or identified. Some of the trainingsamples can be brain image data of subjects who experienced strokesbefore or after the brain image data was captured. Some of the trainingsamples can also be brain image data where stroke events were notdetected or identified. In other words, some of the training samples canbe brain image data of normal subjects, or subjects without any brainconditions indicative of stroke.

The training data sets 624A-N can indicate, for each training sample,whether the sample contains volumes of interest that are indicative ofstroke events. Each of the training samples can be labeled with types ofvolumes of interest, quantities of each volume of interest, and anindication of whether the training sample has one or more stroke events.The training samples can be automatically labeled (e.g., by the computersystem 104 or another computing system). The training samples can alsobe manually annotated and labeled by a clinician, medical professional,or other relevant stakeholder.

The computer system 104 can be configured to perform the techniquesdescribed herein, such as processing and analyzing the brain image data618A-N to identify stroke events. The computer system 104 includeprocessor(s) 600, image processing engine 602, stroke detection engine604, output generator 606, training and model generation engine 607, andcommunication interface 608. In some implementations, one or morecomponents of the computer system 104 can be part of other computersystems, networks, devices, and/or cloud-based services.

The processor(s) 600 can be configured to execute instructions toperform the techniques described throughout this disclosure. The imageprocessing engine 602 can be configured to process the brain image data618A-N. For example, the engine 602 can receive the raw brain image data618A-N from the imaging device(s) 106, the data store 110, and/or theuser device 108. The engine 602 can process the received image data618A-N to remove header data and/or personally identifying information.The engine 602 can store the processed brain image data in the datastore 110. In some implementations, the image processing engine 602 canemploy one or more machine learning models (e.g., one or more of thestroke identification models 622A-N) to process the brain image data618A-N.

The stroke detection engine 604 can use the processed brain image datato determine whether the brain image data depicts volumes of interestindicative of stroke events. In some implementations, the strokedetection engine 604 can retrieve the stroke identification models622A-N from the data store 110. The engine 604 can apply those models622A-N to identify stroke events in the brain image data 618A-N. Forexample, the stroke detection engine 604 can provide the processed brainimage data as input to one or more of the models 622A-N. The models622A-N can output information such as whether stroke events aredetected, what volumes of interest are identified in the brain imagedata, a volume of the brain, a visual representation of the volumes ofinterest overlaying the brain image data, a relative infarct burden onthe total brain volume, a severity of the detected stroke events, aprediction of whether the subject is likely to develop stroke events, aprediction of what side effects/cognitive impairments the subject mayexperience, and/or suggestions for diagnosis and treatment. Output fromthe models 622A-N can be stored in the data store 110 in the patientinformation 620A-N. Output from the models 622A-N can also be used bythe output generator 606.

The output generator 606 can be configured to generate information thatcan be presented to a user at the user device 108. The output generator606 can, for example, generate GUIs and other visual representations ofthe brain image data 718A-N with overlaid volumes of interest. Theoutput generator 606 can also generate notifications, alerts, messages,alarms, or other information that can be presented at the user device108 to notify the relevant user of an identified stroke event, severityscore, risk prediction of developing stroke, and/or prediction ofcognitive or other health impairments that may result from theidentified stroke event. The output generator 606 can transmit outputdirectly to the user device 108 for display. The output generator 606can also store the output in the data store 110, for example as part ofthe patient information 620A-N.

The training and model generation engine 607 can be optional. The engine607 can be configured to train the stroke identification models 622A-N.The engine 607 can retrieve the training data sets 624A-N and use thoseto generate and train the models 622A-N. Training and model generationcan be performed before runtime. During runtime, the engine 607 cancontinuously improve the models 622A-N based on determinations that aremade by the image processing engine 602 and/or the stroke detectionengine 604. Over time, the models 622A-N can more accurately detectstroke events and other information from the brain image data 618A-N. Insome implementations, the training and model generation engine 607 canbe part of a different, remote computer system.

The communication interface 608 can provide for communication betweenthe components described herein.

As described herein, the user device 108 can be used by a clinician,medical professional, or other relevant stakeholder. The user device 108can be any one of a computer, tablet, laptop, mobile phone, smart phone,and/or cellphone. In some implementations, the user device 108 can beintegrated with or part of the imaging device(s) 106 and/or the computersystem 104. Moreover, the user device 108 can be remote from one or moreof the components described herein. For example, the imaging device 106can be locate at a clinic and the user device 108 can be a clinician'smobile phone that is remote from the clinic where a subject's brain isbeing imaged. Once the brain is imaged by the imaging device 106 at theclinic, the imaging device 106 can transmit the image data to the userdevice 108 over a secure communication/network, even though the userdevice 108 is remote from the clinic. The clinician, regardless of wherethey are located, can therefore review the brain image data and anyprocessing/analysis performed by the computer system 104 at the userdevice 108. The user device 108 can include input device(s) 610, outputdevice(s) 612, application interface 614, and communication interface616.

The input device(s) 610 can be configured to receive input from aclinician or other user. The input device(s) 610 can include a touchscreen, mouse, keyboard, microphone, display, or other device forreceiving input.

The output device(s) 612 can be configured to present information at theuser device 108 to the clinician or other user. The output device(s) 612can include a touch screen, speaker, display screen, or other audio orvisual display for outputting information.

The application interface 614 can be configured to present anapplication, software, or other program at the user device 108 forinteracting with the imaging device(s) 106, the data store 110, and thecomputer system 104. For example, an application program can beinstalled at the user device 108 and executed via the applicationinterface 614. The application program can allow for the user to viewthe brain image data 618A-N and to view and interact with output that isgenerated by the computer system 104. For example, the stroke detectionengine 604 of the computer system 104 can identify volumes of interestin a particular subject's brain. The output generator 606 can generate avisual representation of the subject's brain image data with theidentified volumes of interest overlaid thereon. This visualrepresentation can be transmitted to the user device 108 and provided tothe user via the application interface 614. The user can then selectdifferent views of the brain image data (e.g., coronal or sagittalviews) by interacting with the visual representation of the brain imagedata. This input can be transmitted to the computer system 104 such thatthe output generator 606 of the computer system 104 can update thevisual representation of the brain image data.

The communication interface 616 can provide for communication betweenthe components described herein.

FIG. 7 shows a diagram of an exemplary computer processing system thatcan execute the techniques described herein. Computing device 700 isintended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. The mobilecomputing device is intended to represent various forms of mobiledevices, such as personal digital assistants, cellular telephones,smart-phones, and other similar computing devices. The components shownhere, their connections and relationships, and their functions, aremeant to be exemplary only, and are not meant to limit implementationsof the inventions described and/or claimed in this document.

The computing device 700 includes a processor 702, a memory 704, astorage device 706, a high-speed interface 708 connecting to the memory704 and multiple high-speed expansion ports 710, and a low-speedinterface 712 connecting to a low-speed expansion port 714 and thestorage device 706. Each of the processor 702, the memory 704, thestorage device 706, the high-speed interface 708, the high-speedexpansion ports 710, and the low-speed interface 712, are interconnectedusing various busses, and can be mounted on a common motherboard or inother manners as appropriate. The processor 702 can process instructionsfor execution within the computing device 700, including instructionsstored in the memory 704 or on the storage device 706 to displaygraphical information for a GUI on an external input/output device, suchas a display 716 coupled to the high-speed interface 708. In otherimplementations, multiple processors and/or multiple buses can be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices can be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 704 stores information within the computing device 700. Insome implementations, the memory 704 is a volatile memory unit or units.In some implementations, the memory 704 is a non-volatile memory unit orunits. The memory 704 can also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 706 is capable of providing mass storage for thecomputing device 700. In some implementations, the storage device 706can be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product can also containinstructions that, when executed, perform one or more methods, such asthose described above. The computer program product can also be tangiblyembodied in a computer- or machine-readable medium, such as the memory704, the storage device 706, or memory on the processor 702.

The high-speed interface 708 manages bandwidth-intensive operations forthe computing device 700, while the low-speed interface 712 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In some implementations, the high-speed interface 708 iscoupled to the memory 704, the display 716 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 710,which can accept various expansion cards (not shown). In theimplementation, the low-speed interface 712 is coupled to the storagedevice 706 and the low-speed expansion port 714. The low-speed expansionport 714, which can include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet) can be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 700 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as astandard server 720, or multiple times in a group of such servers. Inaddition, it can be implemented in a personal computer such as a laptopcomputer 722. It can also be implemented as part of a rack server system724. Alternatively, components from the computing device 700 can becombined with other components in a mobile device (not shown), such as amobile computing device 750. Each of such devices can contain one ormore of the computing device 700 and the mobile computing device 750,and an entire system can be made up of multiple computing devicescommunicating with each other.

The mobile computing device 750 includes a processor 752, a memory 764,an input/output device such as a display 754, a communication interface766, and a transceiver 768, among other components. The mobile computingdevice 750 can also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 752, the memory 764, the display 754, the communicationinterface 766, and the transceiver 768, are interconnected using variousbuses, and several of the components can be mounted on a commonmotherboard or in other manners as appropriate.

The processor 752 can execute instructions within the mobile computingdevice 750, including instructions stored in the memory 764. Theprocessor 752 can be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 752can provide, for example, for coordination of the other components ofthe mobile computing device 750, such as control of user interfaces,applications run by the mobile computing device 750, and wirelesscommunication by the mobile computing device 750.

The processor 752 can communicate with a user through a controlinterface 758 and a display interface 756 coupled to the display 754.The display 754 can be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface756 can comprise appropriate circuitry for driving the display 754 topresent graphical and other information to a user. The control interface758 can receive commands from a user and convert them for submission tothe processor 752. In addition, an external interface 762 can providecommunication with the processor 752, so as to enable near areacommunication of the mobile computing device 750 with other devices. Theexternal interface 762 can provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces can also be used.

The memory 764 stores information within the mobile computing device750. The memory 764 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 774 can also beprovided and connected to the mobile computing device 750 through anexpansion interface 772, which can include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 774 canprovide extra storage space for the mobile computing device 750, or canalso store applications or other information for the mobile computingdevice 750. Specifically, the expansion memory 774 can includeinstructions to carry out or supplement the processes described above,and can include secure information also. Thus, for example, theexpansion memory 774 can be provide as a security module for the mobilecomputing device 750, and can be programmed with instructions thatpermit secure use of the mobile computing device 750. In addition,secure applications can be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, a computer program product is tangibly embodied in aninformation carrier. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The computer program product can be a computer- ormachine-readable medium, such as the memory 764, the expansion memory774, or memory on the processor 752. In some implementations, thecomputer program product can be received in a propagated signal, forexample, over the transceiver 768 or the external interface 762.

The mobile computing device 750 can communicate wirelessly through thecommunication interface 766, which can include digital signal processingcircuitry where necessary. The communication interface 766 can providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication can occur, forexample, through the transceiver 768 using a radio-frequency. Inaddition, short-range communication can occur, such as using aBluetooth, WiFi, or other such transceiver (not shown). In addition, aGPS (Global Positioning System) receiver module 770 can provideadditional navigation- and location-related wireless data to the mobilecomputing device 750, which can be used as appropriate by applicationsrunning on the mobile computing device 750.

The mobile computing device 750 can also communicate audibly using anaudio codec 760, which can receive spoken information from a user andconvert it to usable digital information. The audio codec 760 canlikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 750. Such sound caninclude sound from voice telephone calls, can include recorded sound(e.g., voice messages, music files, etc.) and can also include soundgenerated by applications operating on the mobile computing device 750.

The mobile computing device 750 can be implemented in a number ofdifferent forms, as shown in the figure. For example, it can beimplemented as a cellular telephone 7870. It can also be implemented aspart of a smart-phone 7872, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichcan be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of thedisclosed technology or of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular disclosed technologies. Certain features that aredescribed in this specification in the context of separate embodimentscan also be implemented in combination in a single embodiment in part orin whole. Conversely, various features that are described in the contextof a single embodiment can also be implemented in multiple embodimentsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described herein as acting in certain combinationsand/or initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a subcombination or variation ofa subcombination. Similarly, while operations may be described in aparticular order, this should not be understood as requiring that suchoperations be performed in the particular order or in sequential order,or that all operations be performed, to achieve desirable results.Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims.

1. A system for automated identification of volumes of interest in brainimage data, the system comprising: one or more processors; and computermemory storing instructions that, when executed by the processors, causethe processors to perform operations comprising: receiving image data ofa brain having header data and voxel values, wherein the voxel valuesrepresent an interruption in blood supply of the brain when imaged;extracting the header data from the image data; populating an array ofcells with the voxel values; applying a segmenting analysis to the arrayto generate a segmented array; applying a morphological neighborhoodanalysis to the segmented array to generate a features relationshiparray, wherein the features relationship array includes features ofinterest in the brain indicative of stroke; identifyingthree-dimensional (3D) connected volumes of interest in the featuresrelationship array; and generating output, for display at a user device,indicating the identified 3D volumes of interest.
 2. The system of claim1, wherein the 3D volumes of interest are at least one of watershedinfarcts and embolic infarcts in the brain indicative of ischemicstroke.
 3. The system of claim 1, wherein the image data of the brain isgenerated by at least one of computed tomography (CT) and MagneticResonance Imaging (MRI).
 4. The system of claim 1, wherein the imagedata of the brain includes time-series data of the brain.
 5. The systemof claim 1, wherein the operations further comprise applying a filteringanalysis to the array to generate a filtered array based on: identifyinga subset of the cells in the array to exclude from the filtered array;and removing the identified subset of the cells to generate the filteredarray.
 6. The system of claim 1, wherein applying the segmentinganalysis comprises segmenting the cells of the array based onphysiological structures of the brain.
 7. The system of claim 1, whereinapplying the morphological neighborhood analysis comprises identifyingfeatures of interest in the segmented array based on connectivity dataamongst the cells in the segmented array.
 8. The system of claim 1,wherein the operations further comprise storing, in a data store, theidentified 3D volumes of interest.
 9. The system of claim 1, whereinidentifying three dimensional (3D) connected volumes of interestcomprises: counting voxel values within a predefined region of the imagedata to infer a volume of the brain; superimposing the identified 3Dvolumes of interest on the within the predefined region of the imagedata; and identifying relative infarct burden in percent of brain volumebased on determining a ratio between the superimposed 3D volumes ofinterest and the inferred volume of the brain.
 10. The system of claim1, wherein generating output comprises generating coronal and sagittalviews of the 3D volumes of interest in the brain image data.
 11. Thesystem of claim 1, wherein generating output comprises: superimposingthe 3D volumes of interest on the brain image data; and tinting the 3Dvolumes of interest in one or more indicia that is different than anindicia of the brain image data.
 12. The system of claim 11, whereintinting the 3D volumes of interest comprises: tinting a first of the 3Dvolumes of interest in a first indicia based on determining that avolume of the first of the 3D volumes of interest is greater than afirst threshold level; and tinting a second of the 3D volumes ofinterest in a second indicia based on determining that a volume of thesecond of the 3D volumes of interest is less than the first thresholdlevel but greater than a second threshold level.
 13. The system of claim1, wherein identifying three-dimensional (3D) connected volumes ofinterest in the features relationship array is based on applying one ormore machine learning models that were trained using training datasetsthat include training brain image data labeled with areas of interestand training brain image data labeled with areas that are not ofinterest.
 14. A method for automatically identifying volumes of interestin brain image data, the method comprising: receiving, by a computingsystem, image data of a brain having header data and voxel values,wherein the voxel values represent an interruption in blood supply ofthe brain when imaged; extracting, by the computing system, the headerdata from the image data; populating, by the computing system, an arrayof cells with the voxel values; applying, by the computing system, asegmenting analysis to the array to generate a segmented array;applying, by the computing system, a morphological neighborhood analysisto the segmented array to generate a features relationship array,wherein the features relationship array includes features of interest inthe brain indicative of stroke; identifying, by the computing system,three-dimensional (3D) connected volumes of interest in the featuresrelationship array; and generating, by the computing system and fordisplay at a user device, output indicating the identified 3D volumes ofinterest.
 15. The method of claim 14, further comprising applying, bythe computing system, a filtering analysis to the array to generate afiltered array based on: identifying a subset of the cells in the arrayto exclude from the filtered array; and removing the identified subsetof the cells to generate the filtered array.
 16. The method of claim 14,wherein applying, by the computing system, the segmenting analysiscomprises segmenting the cells of the array based on physiologicalstructures of the brain.
 17. The method of claim 14, wherein applying,by the computing system, the morphological neighborhood analysiscomprises identifying features of interest in the segmented array basedon connectivity data amongst the cells in the segmented array.
 18. Themethod of claim 14, further comprising storing, by the computing systemand in a data store, the identified 3D volumes of interest.
 19. Themethod of claim 14, wherein identifying, by the computing system, threedimensional (3D) connected volumes of interest comprises: counting voxelvalues within a predefined region of the image data to infer a volume ofthe brain; superimposing the identified 3D volumes of interest on thewithin the predefined region of the image data; and identifying relativeinfarct burden in percent of brain volume based on determining a ratiobetween the superimposed 3D volumes of interest and the inferred volumeof the brain.
 20. The method of claim 14, wherein generating, by thecomputing system, output comprises generating coronal and sagittal viewsof the 3D volumes of interest in the brain image data.